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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
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      "source": [
        "\n# Lasso path using LARS\n\n\nComputes Lasso Path along the regularization parameter using the LARS\nalgorithm on the diabetes dataset. Each color represents a different\nfeature of the coefficient vector, and this is displayed as a function\nof the regularization parameter.\n\n\n"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "print(__doc__)\n\n# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>\n#         Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndiabetes = datasets.load_diabetes()\nX = diabetes.data\ny = diabetes.target\n\nprint(\"Computing regularization path using the LARS ...\")\n_, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)\n\nxx = np.sum(np.abs(coefs.T), axis=1)\nxx /= xx[-1]\n\nplt.plot(xx, coefs.T)\nymin, ymax = plt.ylim()\nplt.vlines(xx, ymin, ymax, linestyle='dashed')\nplt.xlabel('|coef| / max|coef|')\nplt.ylabel('Coefficients')\nplt.title('LASSO Path')\nplt.axis('tight')\nplt.show()"
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